Eric J. Carpenter

Recent synthesis of essential enzymes, such as DNA and RNA polymerases with opposite chirality, has boosted the feasibility of creating mirror-image life. Such life, if ever produced, will undoubtedly be coated by a dense display of glycans (glycoproteins, glycolipids, polysaccharides) built from enantiomers of common monosaccharides. Recognition of mirror-image glycans by extant glycan-binding proteins (GBPs) may be critical for colonization by or immune response to mirror life organisms. We evaluated recognition of enantiomers of common glycans by a diverse set of purified GBPs (plant and human derived), antibodies (including IgM from human plasma), mammalian cells (including immune cells), and organs in live animals. We found that GBP binding to enantiomers of naturally prevalent glycans is widespread. Notably, L-glucose and L-galactose interact with fucose-binding lectins, including DC-SIGN, a C-type lectin expressed on immune cells. These interactions can be inhibited by soluble “natural” glycan ligands and enantiomeric ones confirming specificity. Binding of L-glycans to diverse immune cell repertoires revealed preferences for specific glycan enantiomers. IgM antibodies from human serum showed donor-specific recognition of L-glycans. We propose that the recognition of L-glycans by extant GBPs arises from their co-evolution over millennia with the L-glycans that are present in the glycocalyx of many microorganisms.

We describe a machine-learned (ML) model, MCNet, which predicts interactions between proteins and glycans. MCNet predicted quantitative interactions between glycan-binding proteins (GBPs) and enantiomers of common glycans, which were not part of the original training datasets. l -glycans are rare in nature but are important in consideration of safety of putative mirror-image life-forms. Current ML models that predict properties of glycans from their monosaccharide composition cannot extrapolate properties of mirror glycans. Instead, MCNet uses an atom-level description of the glycan to output an estimate of binding to GBPs. MCNet is trained using data from glycan microarrays and affinity measurements unified using a “fraction bound” parameter. Trained MCNet predicted unexpected binding of l -glucose to some fucose-binding GBPs. Both glycan and lectin arrays conformed these predictions. ML models akin to MCNet reach beyond traditional glycobiology and make it possible to anticipate interaction between biomolecules in mirror-life forms and present-day life-forms.